Updated default settings in test_data.py

deepof/data.py

@@ -870,7 +870,6 @@ class coordinates:
         variational: bool = True,
         reg_cat_clusters: bool = False,
         reg_cluster_variance: bool = False,
-        entropy_radius: float = 0.75,
         entropy_samples: int = 10000,
     ) -> Tuple:
         """
@@ -932,7 +931,6 @@ class coordinates:
             variational=variational,
             reg_cat_clusters=reg_cat_clusters,
             reg_cluster_variance=reg_cluster_variance,
-            entropy_radius=entropy_radius,
             entropy_samples=entropy_samples,
         )
 

deepof/model_utils.py

@@ -284,7 +284,7 @@ class neighbor_cluster_purity(tf.keras.callbacks.Callback):
                 neighbor_number[i] = np.sum(neighborhood)
 
             # Compute weights multiplying neighbor number and target confidence
-            purity_weights = neighbor_number * max_groups
+            purity_weights = neighbor_number * max_groups[random_idxs]
 
             writer = tf.summary.create_file_writer(self.log_dir)
             with writer.as_default():

tests/test_train_utils.py

@@ -57,16 +57,17 @@ def test_get_callbacks(
     loss,
 ):
     runID, tbc, entropy, cycle1c, cpc = deepof.train_utils.get_callbacks(
-        X_train,
-        batch_size,
-        variational,
-        pheno_class,
-        predictor,
-        loss,
-        True,
-        True,
-        True,
-        None,
+        X_train=X_train,
+        batch_size=batch_size,
+        variational=variational,
+        phenotype_class=pheno_class,
+        predictor=predictor,
+        loss=loss,
+        X_val=X_train,
+        cp=True,
+        reg_cat_clusters=False,
+        reg_cluster_variance=False,
+        logparam={"encoding": 2, "k": 15},
     )
     assert type(runID) == str
     assert type(tbc) == tf.keras.callbacks.TensorBoard
@@ -117,7 +118,6 @@ def test_autoencoder_fitting(
         phenotype_class=pheno_class,
         predictor=predictor,
         variational=variational,
-        entropy_radius=0.75,
         entropy_samples=10,
     )
 
@@ -171,7 +171,7 @@ def test_tune_search(
             reg_cat_clusters=True,
             reg_cluster_variance=True,
             entropy_samples=10,
-            logparam=None,
+            logparam={"encoding": 2, "k": 15},
         )
     )[1:]